Publication
Challenges in Data Quality Management for IoT-Enhanced Event Logs
Yannis Bertrand; Alexander Schultheis; Lukas Malburg; Joscha Grüger; Estefanía Serral Asensio; Ralph Bergmann
In: Research Challenges in Information Science - 19th International Conference, RCIS 2025, Proceedings. IEEE International Conference on Research Challenges in Information Science (RCIS-2025), May 20-23, Seville, Spain, Lecture Notes in Business Information Processing (LNBIP), Springer, 2025.
Abstract
Modern organizations make frequent use of Internet of Things (IoT) devices, such as sensors and actuators, to monitor and support their so-called IoT-enhanced Business Processes (BPs). These IoT devices collect vast amounts of data which, when processed appropriately, can yield crucial insights into the working of the BPs. However, IoT data, such as sensor data, is notoriously of poor quality, e.g., suffering from noise or having some missing data points. These problems are referred to as Data Quality Issues (DQIs), which often interfere with the analysis of IoT data in an industrial context. In this paper, we present a list of challenges that have to be tackled to achieve Data Quality (DQ) management in IoT-enhanced event logs. These challenges are derived and refined by leveraging expert knowledge and experience in DQIs within a focus group interview. In addition, we provide directions for solutions to these challenges based on input from the focus group interview and the literature. Finally, we discuss the challenges and their impact on typical DQ management tasks. The insights provided can help guide future research to achieve better DQ in event logs of IoT-enhanced BPs.